Your feedback matters to us! Remember, the projects you influence today will shape the direction of future initiatives. While we value and consider all input, feedback from subscribers and alumni will carry greater weight in our decision-making process.
Which projects most spark your passion? Live training covers classifiers, GPT APIs, and AWS Lambda. Pre-recorded training available for Drug-Target Explorer.
- GPT-assisted ContentModeration
- GPT-boosted GitEaseApp
- Drug-Target Explorer
- AI CodeAssistants -Publication
Live training covers Transcriptomics. Training for Genomics will be added if there is enough demand. Pre-recorded training available for Drug-Target Explorer.
- GPT-enhanced TranscriptomicsApp
- Drug-Target Explorer
- Genomic Explorations
- AI CodeAssistants - Scholarly Publication
Shape your virtual internship experience! Based on our mentors’ expertise and our starter projects, we’ve curated a project shortlist. Have your say - vote for the projects you want to work on. Or reply to this post with suggestions!
|AI CodeAssistants - Scholarly Publication||Drawing from the user research conducted in AI CodeAssistants - UX Emphasis, this project seeks to synthesize and present those insights in a comprehensive publication. This study will highlight the needs and expectations of users interacting with generative AI-powered code assistant tools.||Bioinformatics, Full Stack, Machine Learning, UX-UI|
|GPT-enhanced TranscriptomicsApp||Advancing from the foundation laid by previous interns with an R Shiny transcriptomics pipeline app, this project is set to integrate the GPT API, aiming to enrich both the functionality of the pipeline and the overall user experience.||Bioinformatics, Full Stack, Machine Learning|
|Genomic Explorations||Leverage our mentor's expertise in genomics and delve into a custom-designed project focusing on Genome-Wide Association Studies (GWAS). Specific project objectives and details will be tailored based on student engagement and exploration preferences.||Bioinformatics|
|GPT-assisted Content Moderation||Leveraging the power of web scraping, machine learning classifiers, GPT's language comprehension, and AWS services (Lambda and API Gateway), this project aims to develop a seamless, automated content moderation application that can be easily incorporated by end users.||Machine Learning, Full Stack|
|Drug-Target Explorer||Venturing into the domain of drug-target interactions, this project utilizes machine learning techniques to extract insights from unstructured text in Medline publication abstracts. The aim is not only to comprehend specific drug-target relationships but also to identify wider patterns and themes in the global context.||Machine Learning, Bioinformatics|